from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a    :	Union[str, Any]         =    {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Any         =    ["""LongformerTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Any         =    [
		    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LongformerForMaskedLM""",
		    """LongformerForMultipleChoice""",
		    """LongformerForQuestionAnswering""",
		    """LongformerForSequenceClassification""",
		    """LongformerForTokenClassification""",
		    """LongformerModel""",
		    """LongformerPreTrainedModel""",
		    """LongformerSelfAttention""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Optional[int]         =    [
		    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFLongformerForMaskedLM""",
		    """TFLongformerForMultipleChoice""",
		    """TFLongformerForQuestionAnswering""",
		    """TFLongformerForSequenceClassification""",
		    """TFLongformerForTokenClassification""",
		    """TFLongformerModel""",
		    """TFLongformerPreTrainedModel""",
		    """TFLongformerSelfAttention""",
		]


if TYPE_CHECKING:
		from .configuration_longformer import (
		    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LongformerConfig,
		    LongformerOnnxConfig,
		)
		from .tokenization_longformer import LongformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_longformer_fast import LongformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longformer import (
				    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongformerForMaskedLM,
				    LongformerForMultipleChoice,
				    LongformerForQuestionAnswering,
				    LongformerForSequenceClassification,
				    LongformerForTokenClassification,
				    LongformerModel,
				    LongformerPreTrainedModel,
				    LongformerSelfAttention,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_longformer import (
				    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLongformerForMaskedLM,
				    TFLongformerForMultipleChoice,
				    TFLongformerForQuestionAnswering,
				    TFLongformerForSequenceClassification,
				    TFLongformerForTokenClassification,
				    TFLongformerModel,
				    TFLongformerPreTrainedModel,
				    TFLongformerSelfAttention,
				)

else:
		import sys

		__a    :	List[Any]         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings